Vectorized algorithms for quadtree construction and descent

This paper presents vectorized methods of construction and descent of quadtrees that can be easily adapted to message passing parallel computing. A time complexity analysis for the present approach is also discussed. The proposed method of tree construction requires a hash table to index nodes of a linear quadtree in the breadth-first order. The hash is performed in two steps: an internal hash to index child nodes and an external hash to index nodes in the same level (depth). The quadtree descent is performed by considering each level as a vector segment of a linear quadtree, so that nodes of the same level can be processed concurrently. © 2012 Springer-Verlag.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

Intensified peginterferon α-2a dosing increases sustained virologic response rates in heavy, high viral load hepatitis c genotype 1 patients with high low-density lipoprotein

BACKGROUND AND GOAL: Patients infected with hepatitis C virus (HCV) with elevated low-density lipoprotein (LDL) levels achieve higher sustained virologic response (SVR) rates after peginterferon (PegIFN)/ribavirin treatment versus patients with lower LDL. Our aim was to determine whether SVR rates in patients with low/elevated LDL can be improved by dose intensification. STUDY: In PROGRESS, genotype 1 patients with baseline HCV RNA≥400,000 IU/mL and body weight ≥85 kg were randomized to 48 weeks of 180 μg/wk PegIFN α-2a (40 kDa) plus ribavirin (A: 1200 mg/d; B: 1400/1600 mg/d) or 12 weeks of 360 μg/wk PegIFN α-2a followed by 36 weeks of 180 μg/wk, plus ribavirin (C: 1200 mg/d; D: 1400/1600 mg/d). This retrospective analysis assessed SVR rates among patients with low (<100 mg/dL) or elevated (≥100 mg/dL) LDL. Patients with high LDL (n=256) had higher baseline HCV RNA (5.86×10 IU/mL) versus patients with low LDL (n=262; 4.02×10 IU/mL; P=0.0003). RESULTS: Multiple logistic regression analysis identified a significant interaction between PegIFN α-2a dose and LDL levels on SVR (P=0.0193). The only treatment-related SVR predictor in the nested multiple logistic regression was PegIFN α-2a dose among patients with elevated LDL (P=0.0074); therefore, data from the standard (A+B) and induction (C+D) dose arms were pooled. Among patients with low LDL, SVR rates were 40% and 35% in the standard and induction-dose groups, respectively; SVR rates in patients with high LDL were 44% and 60% (P=0.014), respectively. CONCLUSIONS: Intensified dosing of PegIFN α-2a increases SVR rates in patients with elevated LDL even with the difficult-to-cure characteristics of genotype 1, high baseline viral load, and high body weight. Copyright © 2013 by Lippincott Williams & Wilkins.

Use of algorithms for semi-automatic quantification of pulmonary fibrosis and emphysema

Obtaining a semi-automatic quantification of pathologies found in the lung, through images of high resolution computed tomography (HRCT), is of great importance to aid in medical diagnosis. Paraccocidioidomycosis (PCM) is a systemic disease that affects the lung and even after effective treatment leaves sequels such as pulmonary fibrosis and emphysema. It is very important to the area of tropical diseases that the lung injury be quantified more accurately. In this stud, we propose the development of algorithms in computational environment Matlab® able to objectively quantify lung diseases such as fibrosis and emphysema. The program consists in selecting the region of interest (ROI), and through the use of density masks and filters, obtaining the lesion area quantification in relation to the healthy area of the lung. The proposed method was tested on 15 exams of HRCT of patients with confirmed PCM. To prove the validity and effectiveness of the method, we used a virtual phantom, also developed in this research. © 2013 Springer-Verlag.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Análise do controle de anticoagulação oral em pacientes portadores de próteses valvares cardícas mecânicas por meio de ambulatório especializado: experiência de 10 anos

Pós-graduação em Bases Gerais da Cirurgia - FMB

Comparison of initial loading doses of 5 mg and 10 mg for warfarin therapy

CONTEXTO:A melhor dose para o início do tratamento anticoagulante com varfarina vem sendo debatida nos últimos dez anos. Em nosso meio, não observamos nenhum estudo comparativo quanto a estas características.OBJETIVO:Comparar segurança e eficácia de dois esquemas de dosagem inicial de varfarina para tratamento anticoagulante.MÉTODOS:Foram estudados prospectivamente 110 pacientes de ambos os sexos, consecutivos, com indicação de anticoagulação por tromboembolismo venoso ou arterial. Durante os três primeiros dias de tratamento, estes pacientes receberam doses adequadas de heparina (RT - razão dos tempos - alvo entre 1,5 e 2,5) e 5 mg de varfarina, cuja dose foi reajustada a partir do quarto dia pelo Razão Normatizada Internacional - RNI (alvo entre 2 e 3). Esse grupo foi comparado com série histórica de 110 pacientes que receberam 10 mg nos dois primeiros dias, 5 mg a partir do terceiro dia, com ajuste posterior de dose baseado no RNI. Os desfechos foram: recorrência do tromboembolismo, sangramentos e tempo para alcançar níveis terapêuticos.RESULTADOS:A eficácia, a segurança e o tempo de internação foram similares entre os grupos. O grupo que recebeu 10 mg atingiu níveis terapêuticos mais precocemente (média de 4,5 dias × 5,8 dias), sendo as doses na alta menores e os níveis terapêuticos mais adequados na primeira visita de retorno.CONCLUSÃO:O esquema de dosagem de 10 mg proporcionou menor tempo para alcançar nível terapêutico, com menores doses de varfarina na alta e RNI mais adequado no retorno.

Discovering promising regions to help global numerical optimization algorithms

We have developed an algorithm using a Design of Experiments technique for reduction of search-space in global optimization problems. Our approach is called Domain Optimization Algorithm. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. The Domain Optimization Algorithm approach is based on eliminating non-promising search-space regions, which are identifyed using simple models (linear) fitted to the data. Then, we run a global optimization algorithm starting its population inside the promising region. The proposed approach with this heuristic criterion of population initialization has shown relevant results for tests using hard benchmark functions.

Algorithms for automatic segmentation of bovine embryos produced

In vitro production has been employed in bovine embryos and quantification of lipids is fundamental to understand the metabolism of these embryos. This paper presents a unsupervised segmentation method for histological images of bovine embryos. In this method, the anisotropic filter was used in the differents RGB components. After pre-processing step, the thresholding technique based on maximum entropy was applied to separate lipid droplets in the histological slides in different stages: early cleavage, morula and blastocyst. In the postprocessing step, false positives are removed using the connected components technique that identify regions with excess of dye near pellucid zone. The proposed segmentation method was applied in 30 histological images of bovine embryos. Experiments were performed with the images and statistical measures of sensitivity, specificity and accuracy were calculated based on reference images (gold standard). The value of accuracy of the proposed method was 96% with standard deviation of 3%.

Using Threads to Overcome Synchronization Delays in Parallel Multiple Progressive Alignment Algorithms

Sao Paulo State Research Foundation-FAPESP

A comparison about evolutionary algorithms for optimum-path forest clustering optimization

In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.

OntoSDM: an approach to improve quality on spatial data mining algorithms

The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.